Channel estimation based on learning automata for OFDM systems

The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.     

On the resilience of P2P botnet footprints in the presence of legitimate P2P traffic

Botnet is a distributed platform for illegal activities severely threaten the security of the Internet. Fortunately, although their complicated nature, bots leave some footprints during the C&C communication that have been utilized by security researchers to design detection mechanisms. Nevertheless, botnet designers are always trying to evade detection systems by leveraging the legitimate P2P protocol as C&C channel or even mimicking legitimate peer‐to‐peer (P2P) behavior. Consequently, detecting P2P botnet in the presence of normal P2P traffic is one of the most challenging issues in network security. However, the resilience of P2P botnet detection systems in the presence of normal P2P traffic is not investigated in most proposed schemes. In this paper, we focused on the footprint as the most essential part of a detection system and presented a taxonomy of footprints utilized in behavioral P2P botnet detection systems. Then, the resilience of mentioned footprints is analyzed using three evaluation scenarios. Our experimental and analytical investigations indicated that the most P2P botnet footprints are not resilient to the presence of legitimate P2P traffic and there is a pressing need to introduce more resilient footprints.     

Preparation of novel 2-(2-oxo-2H-chromen-4-yl)-3- arylthiazolidin-4-one derivatives using an efficient ionic liquid catalyst

An eco-friendly procedure for synthesis of 2-(2-oxo-2H-chromen-4-yl)-3-arylthiazolidin-4-one derivatives by three-component reaction of 2-oxo-2H-chromene-4-carbaldehydes, aromatic amines and thioglycolic acid, with tetramethylbutane-1,4-diammonium acetate as a low-cost ionic liquid catalyst under reflux condition is described. The use of an ionic liquid as a catalyst has the advantages of high yields, short reaction time and environmentally friendly reaction media.     

Conversion of CO into CO2 by high active and stable PdNi nanoparticles supported on a metal-organic framework

The solubility of Pd(NO₃)₂ in water is moderate whereas it is completely soluble in diluted HNO₃ solution. Pd/MIL-101(Cr) and Pd/MIL-101-NH₂(Cr) were synthesized by aqueous solution of Pd(NO₃)₂ and Pd(NO₃)₂ solution in dilute HNO₃ and used for CO oxidation reaction. The catalysts synthesized with Pd(NO₃)₂ solution in dilute HNO₃ showed lower activity. The aqueous solution of Pd(NO₃)₂ was used for synthesis of mono-metal Ni, Pd and bimetallic PdNi nanoparticles with various molar ratios supported on MOF. Pd₇₀Ni₃₀/MIL-101(Cr) catalyst showed higher activity than monometallic counterparts and Pd+ Ni physical mixture due to the strong synergistic effect of PdNi nanoparticles, high distribution of PdNi nanoparticles, and lower dissociation and desorption barriers. Comparison of the catalysts synthesized by MIL-101(Cr) and MIL-101-NH₂(Cr) as the supports of metals showed that Pd/MIL-101-NH₂(Cr) outperforms Pd/MIL-101-(Cr) because of the higher electron density of Pd resulting from the electron donor ability of the NH₂ functional group. However, the same activities were observed for Pd₇₀Ni₃₀/MIL-101(Cr) and Pd₇₀Ni₃₀/MIL-101-NH₂(Cr), which is due to a less uniform distribution of Pd nanoparticles in Pd₇₀Ni₃₀/MIL-101-NH₂(Cr) originated from amorphization of MIL-101-NH₂(Cr) structure during the reduction process. In contrast, Pd₇₀Ni₃₀/MIL-101(Cr) revealed the stable structure and activity during reduction and CO oxidation for a long time.     

Novel chiral poly(amide-imide)/surface modified SiO2 nanocomposites based on N-trimellitylimido-l-methionine: Synthesis and a morphological study

In the present investigation, novel poly(amide-imide) (PAI)/SiO₂ nanocomposites (NCs) containing l-methionine moiety in the main chain were prepared via a simple and fast ultrasonic irradiation process. PAI was synthesized by direct poly condensation reaction of N-trimellitylimido-l-methionine with 4,4′-diamino diphenylether in molten tetra-n-butyl ammonium bromide/triphenyl phosphite as a green condensing agent. Due to the high surface energy and tendency for agglomeration, the surface of SiO₂ NPs was modified with chiral diacid. The obtained NCs were characterized by Fourier transform-infrared (FT-IR) spectroscopy, thermogravimetry analysis, X-ray powder diffraction, field emission-scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM). The FT-IR spectroscopy indicated that the chiral diacid as the coupling agent was attached on the surface of SiO₂ NPs. FE-SEM, and TEM images showed that SiO₂ NPs were dispersed rather homogeneously in the PAI matrix.     

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

The Shortest Common Supersequence Problem asks to obtain a shortest string that is a supersequence of every member of a given set of strings. It has applications, among others, in data compression and oligonucleotide microarray production. The problem is NP-hard, and the existing exact solutions are impractical for large instances. In this paper, a new beam search algorithm is proposed for the problem, which employs a probabilistic heuristic and uses the dominance property to further prune the search space. The proposed algorithm is compared with three recent algorithms proposed for the problem on both random and biological sequences, outperforming them all by quickly providing solutions of higher average quality in all the experimental cases. The Java source and binary files of the proposed IBS_SCS algorithm and our implementation of the DR algorithm and all the random and real datasets used in this paper are freely available upon request.     

Predicting Survival Outcomes in the Presence of Unlabeled Data

Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial challenges, which can lead to reduced data collection and, in turn, can complicate subsequent analyses. In contrast, there is often plenty of baseline data available of patients with similar characteristics and background information, e.g., from patients that fall outside the study time window. In this article, we investigate whether we can benefit from the inclusion of such unlabeled data instances to predict accurate survival times. In other words, we introduce a third level of supervision in the context of survival analysis, apart from fully observed and censored instances, we also include unlabeled instances. We propose three approaches to deal with this novel setting and provide an empirical comparison over fifteen real-life clinical and gene expression survival datasets. Our results demonstrate that all approaches are able to increase the predictive performance over independent test data. We also show that integrating the partial supervision provided by censored data in a semi-supervised wrapper approach generally provides the best results, often achieving high improvements, compared to not using unlabeled data.

     

Qualitative analysis of ganoderic acids in Ganoderma lucidum from Iran and China by RP-HPLC and electrospray ionisation-mass spectrometry (ESI-MS)

Ganoderma lucidum is used as a health food and medicine in Far East and produces different ganoderic acids which are well known for their valuable bioactivities. In this study, two different strains of G. lucidum from Iran and China were investigated for ganoderic acids, using reversed phase HPLC (RP-HPLC) in combination with UV and electrospray ionisation-mass spectrometry (ESI-MS). The results showed a relatively low intensity ganoderic acid C2 peak in the chromatogram of the Iranian sample. However, three high intensity peaks attributed to the well known ganoderic acids including ganoderic acids T, Me and H from the Chinese strain. These findings clearly indicated that different strains of G. lucidum could posses a range of active compounds and hence, bioactivities. Moreover, the variation in the triterpenoid components encourages more studies on the rest of bioactive molecules, and also different strains grown in a variety of climatic and geographical conditions.